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Constrained Global Optimization of Expensive Black Box Functions Using Radial Basis Functions.

, and . J. Glob. Optim., 31 (1): 153-171 (2005)

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Surrogate-assisted evolutionary programming for high dimensional constrained black-box optimization.. GECCO (Companion), page 1431-1432. ACM, (2012)Evolutionary Programming for High-Dimensional Constrained Expensive Black-Box Optimization Using Radial Basis Functions.. IEEE Trans. Evol. Comput., 18 (3): 326-347 (2014)Improved Strategies for Radial basis Function Methods for Global Optimization., and . J. Glob. Optim., 37 (1): 113-135 (2007)A quasi-multistart framework for global optimization of expensive functions using response surface models., and . J. Glob. Optim., 56 (4): 1719-1753 (2013)Constrained Global Optimization of Expensive Black Box Functions Using Radial Basis Functions., and . J. Glob. Optim., 31 (1): 153-171 (2005)Hyperparameter Tuning of Random Forests Using Radial Basis Function Models.. LOD (1), volume 13810 of Lecture Notes in Computer Science, page 309-324. Springer, (2022)A two-phase surrogate approach for high-dimensional constrained discrete multi-objective optimization.. GECCO Companion, page 1870-1878. ACM, (2021)Large-Scale Discrete Constrained Black-Box Optimization Using Radial Basis Functions.. SSCI, page 2924-2931. IEEE, (2020)Radial Basis Function and Bayesian Methods for the Hyperparameter Optimization of Classification Random Forests.. ICCSA (Workshops 2), volume 14105 of Lecture Notes in Computer Science, page 508-525. Springer, (2023)Stochastic radial basis function algorithms for large-scale optimization involving expensive black-box objective and constraint functions.. Comput. Oper. Res., 38 (5): 837-853 (2011)